An Introduction to Unstructured Data

The world of unstructured data can look like anything from the binary counterpart to specific words, to pages of IP addresses with no other information attached. The swath of information covered under the unstructured category is the catalyst for the dichotomy of structured vs. unstructured data. Think about your last doctor’s visit. If you’ve been visiting the same doctor for some time, he or she probably has a wealth of information about you: blood test results, height and weight at your given age, your blood pressure range, and lots of other little notes about your comments or complaints and the nature of your visit. Though these charts and records are all written into forms and stored either in a paper file or a computer file, there is a lot of information in forms that today’s number-centric analysis tools simply can’t tackle. If your records have been entered into an electronic database, traditional analytics tools could parse through information about your weight and other numbers listed, but the wealth of doctor’s notes (the unstructured data) would be left behind. Another useful way to understand the grey areas between structured and unstructured data is through the concept of business intelligence. Over the course of decades, businesses have entrenched themselves in more and more technology to collect and store transaction data. Businesses carry plenty of structured information about a range of things: product flow, the customer demographics, changes in the prices of raw goods, income, and expenses. These numbers are well-behaved. As businesses became ever more entrenched in machine-based transactions, including internet purchases, businesses started storing more and more extraneous, less numerical information about transactions, simply because it existed. All of the data, structured or not, cleaned up or not, has been siloed into large collections of information that businesses were scared not to collect, simply because it could one day prove valuable.